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Date:         Wed, 3 Sep 2008 17:08:42 -0400
Reply-To:     Sigurd Hermansen <HERMANS1@WESTAT.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Sigurd Hermansen <HERMANS1@WESTAT.COM>
Subject:      Re: Class variables proc logistic
Comments: To: Monika Nauroth <monika113@GMX.DE>
In-Reply-To:  <48BEF38D.40907@gmx.de>
Content-Type: text/plain; charset="us-ascii"

Monika: Neither alternative makes much of a difference. Stepwise variable selection methods have obvious drawbacks that make them dangerous (in that the results can be misleading) if not simply a waste of time and resources. David Cassell and Peter Flom have presented all the usual caveats on SAS-L and elsewhere. I'll present some examples of how stepwise selection goes wrong at SESUG 2008.

PROC GLMSELECT (LASSO) gives you a better chance of specifying a useful model. For that you need V9.2 or a download from the SAS Web site. S

-----Original Message----- From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Monika Nauroth Sent: Wednesday, September 03, 2008 4:29 PM To: SAS-L@LISTSERV.UGA.EDU Subject: Re: Class variables proc logistic

Does anyone know if the variable selection of a stepwise logistic regression depends on the choice of use of dummy coding or effect coding?

tal schrieb: > On Sep 3, 3:05 am, peterflomconsult...@mindspring.com (Peter Flom) > wrote: >> tal <talila...@GMAIL.COM> wrote >> >>> I'm not sure- but i don't think the sampling is stratified. i have , >>> lets say:20 variables- "is XX important to you?" for each quest the >>> response is >>> 1- important 2- not important 3-no answer. >>> (When i use the class statement- 3 dummy variables are created, but >>> I'm only interested in the first two- the third one is created >>> automatically- but i don't need it- and that's where I have a >>> problem) As i said , for each observation i want to count the number >>> of missing values in the questionnaire- and take it as explanatory >>> variable- but since a dummy variable is created for each var1-var20 >>> the number of missing values is a linear combination of these. Does >>> anybody know how to create only the 2 dummy variables that i need in >>> proc logistic, and drop the third one? >> If your IV has 3 levels, then LOGISTIC will create 2 dummy >> variables;, by default SAS uses EFFECT coding, and dummy (or >> reference) coding, is often better, but I don't think that explains >> your problem >> >> So, could you show your code? >> >> e.g >> >> data today; >> length IV $4; >> input iv $ dv $ weight; >> datalines;; >> Imp yes 100 >> NotI yes 200 >> NA yes 50 >> Imp no 50 >> NotI no 100 >> NA no 50 >> ;;;; >> >> proc logistic data = today; >> class iv (param = ref); >> model dv = iv; >> weight weight; >> run; >> >> creates two dummy variables >> >> Peter >> >> Peter L. Flom, PhD >> Statistical Consultant >> www DOT peterflom DOT com > > Hi! I found another way to do it. Thanks a lot anyway! >


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